title: New measure of insulin sensitivity predicts cardiovascular disease better than HOMA estimated insulin resistance.
abstract: BACKGROUND
  Accurate assessment of insulin sensitivity may better identify individuals at increased risk of cardio-metabolic diseases.
  
  
  OBJECTIVE
  To examine whether a combination of anthropometric, biochemical and imaging measures can better estimate insulin sensitivity index (ISI) and provide improved prediction of cardio-metabolic risk, in comparison to HOMA-IR.
  
  
  METHODS
  Healthy male volunteers (96 Chinese, 80 Malay, 77 Indian), 21 to 40 years, body mass index 18-30 kg/m(2). Predicted ISI (ISI-cal) was generated using 45 randomly selected Chinese through stepwise multiple linear regression, and validated in the rest using non-parametric correlation (Kendall's tau τ). In an independent longitudinal cohort, ISI-cal and HOMA-IR were compared for prediction of diabetes and cardiovascular disease (CVD), using ROC curves.
  
  
  METHODS
  The study was conducted in a university academic medical centre.
  
  
  METHODS
  ISI measured by hyperinsulinemic euglycemic glucose clamp, along with anthropometric measurements, biochemical assessment and imaging; incident diabetes and CVD.
  
  
  RESULTS
  A combination of fasting insulin, serum triglycerides and waist-to-hip ratio (WHR) provided the best estimate of clamp-derived ISI (adjusted R(2) 0.58 versus 0.32 HOMA-IR). In an independent cohort, ROC areas under the curve were 0.77±0.02 ISI-cal versus 0.76±0.02 HOMA-IR (p>0.05) for incident diabetes, and 0.74±0.03 ISI-cal versus 0.61±0.03 HOMA-IR (p<0.001) for incident CVD. ISI-cal also had greater sensitivity than defined metabolic syndrome in predicting CVD, with a four-fold increase in the risk of CVD independent of metabolic syndrome.
  
  
  CONCLUSIONS
  Triglycerides and WHR, combined with fasting insulin levels, provide a better estimate of current insulin resistance state and improved identification of individuals with future risk of CVD, compared to HOMA-IR. This may be useful for estimating insulin sensitivity and cardio-metabolic risk in clinical and epidemiological settings.
authors.affiliation: Saw Swee Hock School of Public Health, Yong Loo Lin School of Medicine, National University of Singapore and NUHS, Singapore, Singapore ; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore and NUHS, Singapore, Singapore.
authors.forename: Kavita
authors.initials: K
authors.lastname: Venkataraman
authors.forename: Chin Meng
authors.initials: CM
authors.lastname: Khoo
authors.forename: Melvin K S
authors.initials: MK
authors.lastname: Leow
authors.forename: Eric Y H
authors.initials: EY
authors.lastname: Khoo
authors.forename: Anburaj V
authors.initials: AV
authors.lastname: Isaac
authors.forename: Vitali
authors.initials: V
authors.lastname: Zagorodnov
authors.forename: Suresh A
authors.initials: SA
authors.lastname: Sadananthan
authors.forename: Sendhil S
authors.initials: SS
authors.lastname: Velan
authors.forename: Yap Seng
authors.initials: YS
authors.lastname: Chong
authors.forename: Peter
authors.initials: P
authors.lastname: Gluckman
authors.forename: Jeannette
authors.initials: J
authors.lastname: Lee
authors.forename: Agus
authors.initials: A
authors.lastname: Salim
authors.forename: E Shyong
authors.initials: ES
authors.lastname: Tai
authors.forename: Yung Seng
authors.initials: YS
authors.lastname: Lee
mesh_terms: D000328:Adult; D000886:Anthropometry / Q000379:methods; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D015331:Cohort Studies; D015309:Glucose Clamp Technique; D006801:Humans; D007333:Insulin Resistance / Q000502:physiology*; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*